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Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Post-deployment changes to large language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI governance when model weights are proprietary. We present a privacy-preserving zk-SNARK-based audit framework that searches for probes designed in the spirit of adversarial examples to amplify logit drift between an approved model and a modified deployment. Our framework explores complementary probe families under different access models. Token-based probes operate in a black-box setting and require only the input interface, tokenizer, and vocabulary.

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